In my post from January about becoming a better runner, I mentioned that I would be posting an update to track my new fitness goals. February got a bit hectic so I decided to make the post in March. Let's see if we can use data to determine if I stuck to my goals!

This summer I ran a lot -- almost every day. It was great to start the morning getting my blood pumping and feeling energized to start the day. However, towards the end of this summer, I started to realize that my form might be unhealthy. Can I use my data to determine what factors lead to a bad form?

I previously did a quick analysis of the data from Asana, the website I use to keep track of all my tasks.
Since it's been about 6 months since then, I thought it would be interesting to take another look to see which trends remained and which changed.

Welcome back to part 2 of my series on basic algo-trading! In this post, I actually use the prediction function we created previously to create a strategy and backtrade it on a mainstream stock! Let's dive in...

The world of trading can be treacherous and practically impossible to keep up with. However, with the recent introduction of commision-free trading platforms like Robinhood, participating in the stock market on a regular basis has become realistic for more people (like me!). After getting used to the terminology and platform, I realized the potential and power services like Robinhood provide with regards to taking control of your financials. But, there was one problem. I never quite felt like I was making informed decisions. Without confidence in my trades, I felt as though I was gambling, rather than trading.

Since I've been using it for about three years now, I would say that statement is pretty accurate. Recently, it dawned on me the amount of potential insights data from Asana could provide about my productivity. Sure enough, there is an 'Export to CSV' but in the actions menu (click the down arrow next to the project name in the center of the screen) for every project! Let's see what lies in my (un)productivity data...

Late night television video clips dominate my consumption habits on youtube. While watching these hosts, I started to notice O'Brien's videos appearing in the recommended videos. At first, I dismissed them as normal considering I was watching a considerable amount of late night shows. However, I began to notice a difference in the way O'Brien (or most likely his team) titles the interview videos. When the guest is a man, the titles seem normal. It is usually something to do with a book they're releasing or a movie they're in. But when the host is a woman, the titles are noticeably more sexual and provocative.

Now, perhaps more than ever, journalism has become instrumental in allowing us to understand current events. We rely on organizations to bring us the truth about the state of our country and world. However, with the rise of clickbait and fake news in this so-called 'post-truth' era, it's critical that our news diet is accurate and that we are not stuck in a content bubble, only consuming what the algorithms deem will generate the most revenue.

Right now, YOLOv2 is the state-of-the-art object detection system. Trained on the VOC and COCO datasets, it processes images in real-time with high accuracy. If you've ever wanted to train YOLOv2 to detect custom objects, this tutorial will walk you through the process from installation to testing.

According to Abernathy MacGregor, a strategic communications firm, as of 2014, more than 75 percent of trades on US stock exchanges are from automated trading systems. If you've ever wondered how they do it, this notebook and the one it references could serve as a quick introduction.